Body Piercing and Metal Allergic Contact Sensitivity: North American Contact Dermatitis Group Data From 2007 to 2010
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the association between piercing and patch test sensitivity to metals (nickel, cobalt, and chromium) in North America. METHODS: A retrospective analysis of 9334 patients tested by the North American Contact Dermatitis Group from 2007 to 2010 was conducted. RESULTS: Nickel sensitivity was statistically associated with at least 1 piercing (risk ratio [RR], 2.52; 95% confidence interval [CI], 2.26-2.81; P < 0.0001) and nickel sensitivity rates increased with the number of piercings (16% for 1 piercing to 32% for ≥ 5 piercings). Prevalence of nickel sensitivity was higher in females (23.2%) than in males (7.1%), but the association with piercing was stronger in males (RR, 2.38; 95% CI, 1.72-3.30; P < 0.0001) than in females (RR, 1.30; CI, 1.13-1.49; P = 0.0002). Crude analysis indicated that cobalt sensitivity was statistically associated with piercing (RR, 1.63; 95% CI, 1.40-1.91; P < 0.0001); however, stratified analysis showed that this relationship was confounded by nickel. After adjusting for nickel sensitivity, the adjusted risk ratio for piercing and cobalt was 0.78 (not significant). Chromium sensitivity was negatively associated with piercing (RR, 0.60; 95% CI, 0.48-0.75; P < 0.0001). CONCLUSIONS: Piercing was statistically associated with sensitivity to nickel. This relationship was dose dependent and stronger in males. Cobalt sensitivity was not associated with piercing when adjusted for nickel. Chromium sensitivity was negatively associated with piercing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".